The Data-Powered Positive Deviance Handbook
Basma Albanna & Andreas Pawelke with Jeremy Boy & Andreas Gluecker
Abstract
Positive Deviance (PD) is based on the observation that in every community or organization, there are a few individuals who achieve significantly better outcomes than their peers, despite having similar challenges and resources. These individuals are referred to as positive deviants, and adopting their solutions is what is referred to as the PD approach.
The method described in this Handbook follows the same logic as the PD approach but uses pre-existing, non-traditional data sources instead of – or in conjunction with – traditional data sources. Non-traditional data in this context broadly refers to data that is digitally captured (e.g. mobile phone records and financial data), mediated (e.g. social media and online data), or observed (e.g. satellite imagery). The integration of such data to complement traditional data sources generally used in PD is what we refer to as Data Powered Positive Deviance (DPPD).
This Handbook provides a portable, step-by-step guide to applying the DPPD method: a mixed methods approach that relies on this combination of traditional and non-traditional data for identifying grassroots solutions to complex development problems.
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